{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第四周基础作业"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=black size=4 face=雅黑>导入必要的工具包</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -*- coding:utf-8 -*-\n",
    "import sys\n",
    "#reload(sys)\n",
    "#sys.setdefaultencoding(\"utf-8\")\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=black size=4 face=雅黑>读取数据</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"boston_housing.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "一、样本数N：506\n",
    "\n",
    "二、特征维数：13，每个特征的数据类型，非空值的样本数目\n",
    "\n",
    "CRIM：人均犯罪率 ；\n",
    "ZN：住宅用地的比例（对超过两万五千平方英尺的区域） ；整数\n",
    "INDUS：城镇中非零售营业比例；\n",
    "CHAS：是否靠近Charles河的边界；整数\n",
    "NOX：一氧化氮浓度 ；\n",
    "RM：住宅平均房间数；\n",
    "AGE：1940年之前建造的自住房屋的比例；\n",
    "DIS：距离波士顿五个中心的加权距离；\n",
    "RAD：高速公路的便利指数（索引）；整数\n",
    "TAX ：每一万美元财产的全额财产税率； 整数\n",
    "PTRATIO ：城乡师生比例；PTRATIO\n",
    "B ：黑人比例\n",
    "LSTAT ：低收入人群比例\n",
    "三、标签y MEDV ：自住房屋价格的中值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 对连续型特征，可以用哪个函数可视化其分布？（给出你最常用的一个即可），并根据代码运行结果给出示例。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15fc3e997f0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##直方图配合提琴形\n",
    "figure, axes = plt.subplots(1, 2, figsize=(6, 4))\n",
    "sns.distplot(df[\"MEDV\"], bins=30, kde=True, ax=axes[0])\n",
    "sns.violinplot(data=df[\"MEDV\"], ax=axes[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 对两个连续型特征，可以用哪个函数得到这两个特征之间的相关性？根据代码运行结果，给出示例。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15fc41f5cd0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#相关矩阵\n",
    "#get the names of all the columns\n",
    "cols = df.columns \n",
    "\n",
    "data_corr = df.corr()\n",
    "sns.heatmap(data_corr,annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 如果发现特征之间有较强的相关性，在选择线性回归模型时应该采取什么措施。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如特征之间相关性较强，应降低特征之间的关系对线性回归的影响，可以通过PCA降维（特征层面）或加正则项（模型层面），但同时也应考虑特征之间的组合关系，比如房子的长和宽作为两个特征参与模型的构造，不如把其相乘得到面积然后作为一个特征来进行求解，这样在特征选择上就做了减少维度的工作。如俩个特征相关性特别强，则可以只保留一个特征或在正则训练模型时淘汰一部分特征，以达到降维的作用。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. 当采用带正则的模型以及采用随机梯度下降优化算法时，需要对输入（连续型）特征进行去量纲预处理。课程代码给出了用标准化（StandardScaler）的结果，请改成最小最大缩（MinMaxScaler）去量纲 ，并重新训练最小二乘线性回归、岭回归、和Lasso模型。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 490 entries, 0 to 505\n",
      "Data columns (total 23 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   CRIM      490 non-null    float64\n",
      " 1   ZN        490 non-null    float64\n",
      " 2   INDUS     490 non-null    float64\n",
      " 3   CHAS      490 non-null    float64\n",
      " 4   NOX       490 non-null    float64\n",
      " 5   RM        490 non-null    float64\n",
      " 6   AGE       490 non-null    float64\n",
      " 7   DIS       490 non-null    float64\n",
      " 8   TAX       490 non-null    float64\n",
      " 9   PTRATIO   490 non-null    float64\n",
      " 10  B         490 non-null    float64\n",
      " 11  LSTAT     490 non-null    float64\n",
      " 12  RAD_1     490 non-null    uint8  \n",
      " 13  RAD_2     490 non-null    uint8  \n",
      " 14  RAD_3     490 non-null    uint8  \n",
      " 15  RAD_4     490 non-null    uint8  \n",
      " 16  RAD_5     490 non-null    uint8  \n",
      " 17  RAD_6     490 non-null    uint8  \n",
      " 18  RAD_7     490 non-null    uint8  \n",
      " 19  RAD_8     490 non-null    uint8  \n",
      " 20  RAD_24    490 non-null    uint8  \n",
      " 21  MEDV      490 non-null    float64\n",
      " 22  log_MEDV  490 non-null    float64\n",
      "dtypes: float64(14), uint8(9)\n",
      "memory usage: 61.7 KB\n"
     ]
    }
   ],
   "source": [
    "##最小最大去量纲\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# 删除y大于等于50的样本（保留小于50的样本）\n",
    "df = df[df.MEDV < 50]\n",
    "\n",
    "#输出样本数和特征维数\n",
    "#print(df.shape)\n",
    "# 从原始数据中分离输入特征x和输出y\n",
    "y = df['MEDV']\n",
    "X = df.drop('MEDV', axis = 1)\n",
    "\n",
    "# 尝试对y（房屋价格）做log变换，对log变换后的价格进行估计\n",
    "log_y = np.log1p(y)\n",
    "# RAD的含义是距离高速公路的便利指数。虽然给的数值是数值型，但实际是索引，可换成离散特征/类别型特征编码试试。\n",
    "X[\"RAD\"].astype(\"object\")\n",
    "X_cat = X[\"RAD\"]\n",
    "X_cat = pd.get_dummies(X_cat, prefix=\"RAD\")\n",
    "\n",
    "X = X.drop(\"RAD\", axis = 1)\n",
    "\n",
    "#特征名称，用于保存特征工程结果\n",
    "feat_names = X.columns\n",
    "\n",
    "# 分别初始化对特征和目标值的标准化器\n",
    "ss_X = MinMaxScaler()\n",
    "ss_y = MinMaxScaler()\n",
    "\n",
    "ss_log_y = MinMaxScaler()\n",
    "\n",
    "# 分别对训练和测试数据的特征以及目标值进行标准化处理\n",
    "# 对训练数据，先调用fit方法训练模型，得到模型参数；然后对训练数据和测试数据进行transform\n",
    "X = ss_X.fit_transform(X)\n",
    "\n",
    "#对y做标准化不是必须\n",
    "#对y标准化的好处是不同问题的w差异不太大，同时正则参数的范围也有限\n",
    "y = ss_y.fit_transform(y.values.reshape(-1, 1))\n",
    "log_y = ss_y.fit_transform(log_y.values.reshape(-1, 1))\n",
    "\n",
    "fe_data = pd.DataFrame(data = X, columns = feat_names, index = df.index)\n",
    "fe_data = pd.concat([fe_data, X_cat], axis = 1, ignore_index=False)\n",
    "\n",
    "#加上标签y\n",
    "fe_data[\"MEDV\"] = y\n",
    "fe_data[\"log_MEDV\"] = log_y\n",
    "\n",
    "#保存结果到文件\n",
    "fe_data.to_csv('FE_boston_housing.csv', index=False)\n",
    "fe_data.head()\n",
    "fe_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The L2_r2 score of RidgeCV on test is 0.693253301059839\n",
      "The L2_r2 score of RidgeCV on train is 0.8000790186112842\n",
      "The L1_r2 score of LassoCV on test is 0.6904160529435242\n",
      "The L1_r2 score of LassoCV on train is 0.8001054738751575\n",
      "The r2 score of LinearRegression on test is 0.691834670320638\n",
      "The r2 score of LinearRegression on train is 0.8001507257439991\n"
     ]
    }
   ],
   "source": [
    "#训练最小二乘线性回归、岭回归、和Lasso模型\n",
    "#读取数据\n",
    "dg = pd.read_csv(\"FE_boston_housing.csv\")\n",
    "# 从原始数据中分离输入特征x和输出y\n",
    "y = dg[\"MEDV\"]\n",
    "X = dg.drop([\"MEDV\", \"log_MEDV\"], axis = 1)\n",
    "#特征名称，用于后续显示权重系数对应的特征\n",
    "feat_names = X.columns\n",
    "#将数据分割训练数据与测试数据\n",
    "from sklearn.model_selection import train_test_split\n",
    "# 随机采样20%的数据构建测试样本，其余作为训练样本\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=33, test_size=0.2)\n",
    "\n",
    "\n",
    "#岭回归／L2正则\n",
    "from sklearn.linear_model import  RidgeCV\n",
    "#1. 设置超参数（正则参数）范围\n",
    "alphas = [ 0.01, 0.1, 1, 10,100]\n",
    "#2. 生成一个RidgeCV实例\n",
    "ridge = RidgeCV(alphas=alphas, store_cv_values=True)  \n",
    "\n",
    "#3. 模型训练\n",
    "ridge.fit(X_train, y_train)    \n",
    "\n",
    "#4. 预测\n",
    "y_test_pred_ridge = ridge.predict(X_test)\n",
    "y_train_pred_ridge = ridge.predict(X_train)\n",
    "\n",
    "from sklearn.metrics import r2_score\n",
    "# 评估，使用r2_score评价模型在测试集和训练集上的性能\n",
    "print ('The L2_r2 score of RidgeCV on test is', r2_score(y_test, y_test_pred_ridge))\n",
    "print ('The L2_r2 score of RidgeCV on train is', r2_score(y_train, y_train_pred_ridge))\n",
    "\n",
    "#### Lasso／L1正则\n",
    "from sklearn.linear_model import LassoCV\n",
    "#超参数复用L2\n",
    "#生成LassoCV实例（默认超参数搜索范围）\n",
    "lasso = LassoCV()  \n",
    "\n",
    "#3. 训练（内含CV）\n",
    "lasso.fit(X_train, y_train)  \n",
    "\n",
    "#4. 测试\n",
    "y_test_pred_lasso = lasso.predict(X_test)\n",
    "y_train_pred_lasso = lasso.predict(X_train)\n",
    "\n",
    "\n",
    "# 评估，使用r2_score评价模型在测试集和训练集上的性能\n",
    "print ('The L1_r2 score of LassoCV on test is', r2_score(y_test, y_test_pred_lasso))\n",
    "print ('The L1_r2 score of LassoCV on train is', r2_score(y_train, y_train_pred_lasso))\n",
    "\n",
    "# 线性回归\n",
    "#class sklearn.linear_model.LinearRegression(fit_intercept=True, normalize=False, copy_X=True, n_jobs=1)\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# 1.使用默认配置初始化学习器实例\n",
    "lr = LinearRegression()\n",
    "\n",
    "# 2.用训练数据训练模型参数\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "# 3. 用训练好的模型对测试集进行预测\n",
    "y_test_pred_lr = lr.predict(X_test)\n",
    "y_train_pred_lr = lr.predict(X_train)\n",
    "#测试集\n",
    "print ('The r2 score of LinearRegression on test is', r2_score(y_test, y_test_pred_lr))\n",
    "#训练集\n",
    "print ('The r2 score of LinearRegression on train is', r2_score(y_train, y_train_pred_lr))"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. 代码中给出了岭回归（RidgeCV）和Lasso（LassoCV）超参数（alpha_）调优的过程，请结合两个最佳模型以及最小二乘线性回归模型的结果，给出什么场合应该用岭回归，什么场合用Lasso，什么场合用最小二乘。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "首先只要数据线性相关，LinearRegression是我们的首选,如果发现拟合或者预测的不够好，再考虑其他的线性回归模型。在数据线性相关，且用最小二乘线性回归拟合的不是很好，需要正则化，可以考虑使用RidgeCV回归, 如果输入特征的维度很高,而且是稀疏线性关系的话， RidgeCV就不太合适,考虑使用Lasso回归类模型。对于高纬的特征数据,尤其是线性关系是稀疏的，或者是要在一堆特征里面找出主要的特征，Lasso回归更加适合。  "
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